The Comprehensive Guide to Product Lifecycle Management

Every day when I wake up, I open my laptop, read my emails, and check the news (also the AI news). And every day I see new models, new research papers, and new projects. There's a lot of things happening.

I feel haste. I feel urgency. I have the feeling that I have to do something with this information and also a little bit of FOMO. I see other companies taking actions and I think maybe we should do too.

All this creates a kind of pentup energy that I don’t really know where to put. It makes me feel like I should do something. And like every person in business I fall back on the most familiar reflex when something becomes too big, too fast, or too complex to handle: outsource it, hire help, make it someone else’s problem.

And with AI I think that it's the wrong way to look around about it.

Outsourcing AI Thinking Is Dangerous

We see this with many of our clients. They bring in external teams like us to build software, just like they hire plumbers to fix blocked pipes. They don’t train plumbers internally because it’s inefficient, and they don’t stand up full development teams from scratch because it takes enormous time, cost, and organisational effort. In most cases, outsourcing is simply the fastest and least disruptive way to keep the business running.

But the moment you hand it off, you also hand off the learning that comes with it. The thinking, the decisionmaking, the conversations you should be having internally about AI, those end up happening somewhere else, with someone who isn’t living your organisation’s reality.

And that’s the real risk. AI is topic simply too big, and it’s going to change the way we work too deeply, for any organization to outsource the understanding and the learning to an entity outside your own walls.

Why AI Is Different From Every "Disruptive" Technology Before

When we talk about technology, we often throw around the word “disruptive,” but AI genuinely earns it. Not because it’s louder or faster, but because it changes where work happens and who can do it. So the question becomes: why is AI different from all the other technologies we once thought would change everything? For me, it comes down to three simple but profound shifts.

1. Humans work inside systems, AI works across them

We all work in systems. Whether it’s CRM, email, development tools, ERP (you name it) our daily work happens inside these structured applications. But the real effort, the part no system truly handles, lives between those tools.

Whenever something is too complex or too unstructured to automate, we put humans there. They make judgment calls, chase information, talk to multiple teams, fix issues, and move processes from status A to status B. In practice, people act as the connective tissue that keeps all these systems aligned and moving.

They are the glue between applications, and that’s exactly the space where AI is starting to make an impact.

Those inbetween roles, those loops are now increasingly automatable. Five years ago this simply wasn’t realistic. Today, AI can take over more of that glue work, the work currently done by people, and in the future this will only accelerate.

2. AI automates what was previously not automatable

The AI market can be sliced in many ways, but the distinction that works best for me is this:

On one side, you have tools, the more traditional, incremental form of software development. A new feature here, a small improvement there, something that makes a product 5% better or a bit nicer to use. In the AI world, that’s things like translation features, summarisation buttons, or a smart autocomplete that fills in a few fields for you. Useful, but ultimately just extensions of what software has always done.

Then you have agents. And I’ll be honest, I don’t even like the word, because everyone calls everything an “agent” these days, and 9 out of 10 times it isn’t one. Because if you look carefully at what a true agent actually is, it’s something very different.

It's a software system that can take unstructured information, turn it into its own todo list, execute that list (or ask other AIs to do it), move between systems, pull data from your CRM, make decisions, and then produce structured, meaningful output. That’s not a nicer tool. That’s a different category of software entirely.

Because the truth is, our work is really just a bundle of tasks. Some of those tasks are incredibly difficult to automate (like building relationships, reading a room, having dinner with a client if you are a salesperson). Human connection isn’t something AI can replace so those parts of the task bundle are, for now, safe.

But the small, repetitive administrative tasks? Current AI systems can already automate many of these or help you complete them much faster. And everything in between. Those mixed bundles of judgment, admin, and minor decisions, AI will become increasingly capable of handling. And that capability will only continue to grow.

But how will people experience these shifts? How will we guide them through it? How will we make sure this transition strengthens, rather than unsettles, the organization?

Navigating the Human Side of an AI-Driven Workflow

Certain tasks will naturally shift from humans to AI, we see that happening little by little everyday. One or two tasks here, a small process there, nothing dramatic at first. The work doesn’t disappear. It simply stops being done by people.

And that’s where the real conversation begins. Because while tasks may move, the people doing them don’t vanish. Their identity, their sense of contribution, and the value they bring to the organisation are tied to that work. So we need to start talking about these things now, openly and honestly.

The Financial Pressure

A little while ago, we visited one of our retail clients. In many ways, their organisation was wellstructured: each department ran efficiently within its own vertical, people knew what they were responsible for, and they solved problems quickly. But the moment work had to move between those verticals, everything started to slow down.

They had people manually moving information from one system to another. Typing data into Excel, copying it into Outlook, pulling information back out of Outlook, adjusting formats, fixing small inconsistencies (“this should be five numbers instead of six”), and repeating that process dozens of times a day. None of it was strategic work. All of it was essential work.

And this is the reality for many organisations. These manual gluetasks easily cost €50,000 per person per year. Now imagine an AI system that can do 80% of that work for €500 a year.

What would you do then? What would your customers do? What would any business do if they had a hundred people performing those types of tasks?

This is where the financial motivation becomes impossible to ignore.

People Need To Be Part Of The Plan

This is where the human side becomes just as important as the financial one. If you’re not actively planning for how AI and automation will be introduced in your organisation, how people will be trained, how their roles may evolve, and how this new technology will find a place that feels fair and comfortable, then people simply get left out of the story.

Because if the discussion reaches the board without that human context, it turns into a numbersonly decision. On a spreadsheet, €550,000 versus €500 is not a dilemma; it’s a conclusion. And when that comparison involves dozens or hundreds of people, the choice becomes even more obvious.

That’s why it’s essential to build a human plan alongside the financial logic. People need to understand what’s coming, how it affects their work, and what their future looks like in an AIenabled organization. This shift is happening whether we want it or not but how people experience it is still very much in our hands.

The First Steps Every Company Should Take

We need to start having real conversations about AI, not because it's trendy, but because the world around us is moving whether we participate or not. Two years ago, for some organisations, “AI” meant buying a chatbot or automating a single workflow. But every day I open my laptop, read the news, or check new research, and the capabilities have grown again. Things we thought were impossible last year are suddenly standard.

Other companies are already acting on this. And if we aren’t even aware of what’s becoming possible, we can’t expect our organization to generate the ideas or innovations we’ll need to stay competitive.

The best ideas always come from people. But only if those people are informed, involved, and part of the conversation.

1. Remove the Fear Around Automation

Automation is already happening all around us, and one of the most important things organisations can do is make it a topic people feel safe discussing. It doesn’t have to be a scary word. In many industries (manufacturing is a great example) automation has been evolving for decades. Work that was once done with hammers, chisels, and manual effort is now done by robots, and often done better.

So automation itself isn’t the problem. The real challenge is helping people understand what it means for them. You need a plan for how your organisation will adapt, how roles might evolve, and how people will be supported through that change. When automation is part of an honest, structured conversation, it becomes something you manage, not something you fear. And that brings me to the second point.

2. Be Transparent

Transparency becomes critical the moment you start moving toward AI adoption. People need to understand what is happening, why it is happening, and how it will affect the way they work. When organisations stay quiet or vague, uncertainty fills the gaps. And uncertainty quickly turns into fear.

That’s why you need a clear roadmap. Not a perfect one, but one that shows direction, intent, and honesty. Let people see how you’re approaching this project, what decisions are being made, and where they fit into the story.

If we are upfront about the scale of the transformation, people can prepare, contribute, and adapt. But if we keep the process behind closed doors, AI becomes something that “happens to them” rather than something they are part of.

3. Enable Organisational Insight

Before you can do any of this successfully, you need a clear understanding of your own organisation. Your processes, your data, your people, and how work actually gets done. This has never been more important, because AI is now capable of automating the kinds of work that were previously considered impossible to automate.

Most companies have beautifully documented process diagrams and welldefined application flows. But everything between those flows, the real daytoday work, the unwritten parts of your job description, the informal steps people take to keep things moving? Those are rarely captured anywhere. And it’s exactly in that unstructured space where AI is beginning to make its impact.

Act or Be Acted Upon

Are you going to be the kind of organisation that embraces AI intentionally? One where people are informed, aligned, and understand how the company plans to work with AI as its capabilities grow?

Or will you become the organisation where AI simply “happens” to you? Two years pass, competitors have embraced AI, costs have dropped, efficiency has soared, and suddenly customers are asking why you can’t keep up.

If you reach that point, you no longer have the time or space to create your own framework, your own human story, or your own way of adapting to these changes. You’re forced into action instead of choosing it. And by not acting, by not even beginning the discussion, you’re still making a choice.

You’re choosing to end up in the group where AI happens to you rather than through you. And that is a position no organisation wants to find itself in, yet it is the silent reality many companies are drifting toward.

AI Is a Train Already Moving

AI is getting more capable every day, and ignoring it won’t slow it down. It’s a train already in motion, whether we like it or not. The only real question is whether we choose to take control of how it impacts us.

That starts with getting informed, involving more people, and having the conversations that matter. And I genuinely believe we are already taking good steps in that direction at CLEVR. More people are engaged, more discussions are happening, and that’s exactly what we need.

So talk about it. Think about it. Discuss it with your colleagues. The more we share our thoughts and questions, the better prepared we become.

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AI is a tsunami, it’s coming. The question is not whether you’ll ride the wave, but how well you’ll ride it.

AI is one of the most transformative technologies of our time. It’s revolutionizing industries, unlocking new efficiencies, and driving innovation.

But with great power comes great responsibility. The enthusiasm surrounding AI often comes with concerns about its fairness, transparency, and long-term impact on jobs and society.

The challenge for businesses isn’t just about harnessing AI’s capabilities, it’s about doing so responsibly. Incorporating AI governance, explainability, and compliance is how companies ensure they ride the wave effectively without losing control.

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AI Makes Your Job Better: Empowerment, instead of Replacement

One of the biggest misconceptions about AI is that it’s designed to replace people. In reality, AI is about empowerment, it’s about helping people make smarter decisions, faster, and with more insight.

Take OOE dashboards, for example. They give you the data, but what do you do with it? AI doesn’t just deliver information; it turns data into actionable insights, helping people interpret and act on that information.

The real value of AI lies in its ability to amplify human judgment, not replace it. AI supports the human decision-making process by filtering through data, suggesting options, and predicting outcomes, but ultimately, humans remain in the driver’s seat.

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Building a Responsible AI-Driven Organization

1. Michael Wade’s Three Core Capabilities for Responsible AI

To successfully navigate AI’s integration into business, leaders must embrace Michael Wade’s three core competencies:

Hyperawareness

AI allows businesses to sit on a goldmine of data. To harness this power, companies must be aware of how and where data flows through their systems. This awareness leads to more informed, proactive decisions.

Information-based decision thinking

AI empowers leaders to move beyond intuition. With data-driven insights, leaders can make decisions that are grounded in reality, not just experience. This thinking is key to unlocking AI’s true potential.

Fast execution

AI helps organizations respond faster to changing market conditions, enhancing agility. It’s about scaling decisions quickly, adapting processes, and innovating at speed, all while keeping the core intact. Fast execution is where human oversight meets AI’s analytical power.

These capabilities form the foundation for responsible AI adoption, but awareness and speed mean little without trust.

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2. Establish Explainability to Build Trust in AI

AI works best when people trust it. But trust doesn’t come automatically. If AI decisions aren’t explainable, it’s easy for businesses to lose credibility.

Explainability ensures that everyone involved, from employees to customers, understands how decisions are being made.

In sectors like banking, healthcare, or manufacturing, where decisions directly impact people’s lives, this trust is critical. When this explainability is embraced, AI models become more transparent and understandable. This clarity builds trust, making AI an enabler of responsible innovation, not a black-box risk.

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3. Create Strong Governance to Guide AI Use

As AI systems become more integrated into business processes, governance plays a pivotal role. AI is not just a technology to be deployed in isolation; it’s a strategic capability that must be managed and overseen at every level of the organization.

Without strong governance, AI can quickly become a risk rather than an asset. This governance framework ensures that AI is used within ethical boundaries, aligning it with the company’s values and strategic goals.

Leaders must establish clear guidelines on how AI is implemented, who is responsible for decisions, and how it is regulated. By setting up proper governance, businesses can mitigate risks like bias, unethical behavior, and unintended harm.

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4. Make AI a Strategic Capability, Not a Technical Project

AI is too important to be treated merely as a technical experiment. For AI to deliver real enterprise value, it must be integrated into the company’s strategic goals.

Too often, AI is treated as a standalone project or a technological trend. Implementing AI without a clear vision for how it aligns with business operations can lead to fragmented deployments.

AI isn’t just about technology; it’s about strategy.

The key to scalable AI adoption is treating AI as a strategic enabler for business transformation. Leaders must ensure that AI aligns with the company’s long-term vision and drives improvements in efficiency, customer satisfaction, and sustainability.

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5. Lead the Organization Toward AI Adoption and Trust

The success of any AI initiative depends on leadership. Without strong leadership, AI can quickly become a tool for unintended consequences, such as bias, or loss of control.

It must be ensured that AI is deployed responsibly, as a tool to enhance decision-making, not just efficiency or profit.

Leaders also need to provide the guidance and framework to allow employees to trust AI. This means fostering a culture where AI amplifies human judgment, rather than replacing it. When AI is seen as a tool for empowerment, helping people make better decisions, faster, and more accurately, it becomes a force for positive transformation.

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AI doesn’t replace judgment, it amplifies it

AI doesn’t replace human decision-making; it supports and amplifies it. Integrated into business operations, AI doesn’t take over decisions, it provides the context and insight to make them better.

The future of AI lies in collaboration between human intuition and machine learning. As long as the human factor stays in the driver’s seat, organizations can trust AI to enhance, not erode, control.

Ultimately, responsible AI isn’t just about algorithms or compliance, it’s about people trusting technology enough to use it wisely. AI empowers people to act with greater clarity, not less control. That’s the real measure of transformation.

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Originally published here.

SAP S/4HANA is the future, and for most CIOs the pressure is on. Mainstream maintenance for SAP Business Suite systems winds down in 2027, with only limited and increasingly costly options beyond that, making inaction less of a strategy and more postponement with rising risk.

But how do you even begin a SAP S/4HANA migration when you know you are about to let go of years of ABAP customizations, Z‑code, and embedded workflows that keep day-to-day operations running?

SAP is explicit about this. Much of that custom logic cannot move across unchanged. It must be identified, adapted, or removed entirely, leaving CIOs exposed to a parallel challenge alongside the migration itself.

Critical business logic that once lived safely inside the core must be rebuilt elsewhere and rebuilt fast enough to avoid disrupting operations. But where does this logic go, and how do you reintroduce it without recreating the very technical debt S/4HANA is designed to eliminate?

The clean core strategy: Why SAP prioritizes scalability and long-term agility

Large enterprise ERP systems tend to accumulate complexity in ways that eventually work against the organization. Over years of customization, business logic becomes deeply embedded in the core through custom code, enhancements, and tightly coupled integrations, making systems harder to upgrade, slower to scale across regions, and more expensive to maintain.

The clean core approach is SAP’s response to this pattern. By keeping the ERP core standard and upgrade stable, organizations gain the ability to adopt new SAP releases more predictably, roll out changes consistently across global operations, and prevent technical debt from compounding over time.

SAP has two extensibility models for this purpose. In‑app extensions that live inside S/4HANA but are not modifying the standard core (best for lightweight changes that stay close to standard processes like BAdIs, Fiori app extensions, or ABAP RAP), and side‑by‑side extensions that live outside S/4HANA, typically on SAP BTP. These are separate applications, workflows, and integrations that communicate with S/4HANA through APIs and events (best for complex business applications, AI/ML services and integrations, multi-system data orchestration, customer portals and mobile apps).

According to SAP, innovation does not disappear in this model. It simply moves to places where it can evolve without putting the stability of the core at risk. But how true is that in practice?

SAP extensibility vs. Low code: Choosing the right execution model for business-critical applications

SAP’s direction on extensibility is architecturally sound. By introducing clear extension models SAP enables organizations to keep the S/4HANA core stable while still allowing innovation around it. From a scalability, upgradeability, and long-term agility perspective, this approach makes sense.

However, clearly defining where custom logic should live does not always translate into the speed, sustainability, and predictability required to deliver business‑critical capabilities.

Why business-critical extensions increase technical debt in legacy SAP environments

Even when built side by side, native SAP extensions typically rely on traditional development approaches using ABAP, UI5, or CAP. These remain high-code efforts with longer design, build, and test cycles than business timelines allow, that over time can:

• slow down delivery
• make costs harder to predict
• increase dependency on scarce specialist skills
• add governance and coordination overhead
• shift CIO focus from innovation to capacity management and long-term maintenance risk

How low code helps CIOs reduce technical debt and regain speed, predictability, and control

Low code platforms such as Mendix are designed to operate fully side‑by‑side with SAP S/4HANA, integrating through standard APIs and events while keeping the digital core clean. Instead of treating clean‑core extensibility as a purely technical exercise, low code helps CIOs:

1. Restore critical business logic faster 

Visual development models, reusable components, and rapid iteration cycles allow teams to rebuild workflows much faster than traditional high‑code approaches. This helps organizations reintroduce essential business functionality without delaying operations during or after S/4HANA migration.

2. Reduce dependency on scarce SAP skills 

Low code changes how delivery teams are structured. Rather than relying exclusively on scarce ABAP, UI5, or CAP expertise, CIOs can form cross‑functional teams that combine business knowledge with IT governance. This expands delivery capacity and reduces risk tied to specialized staffing constraints.

3. Enable modular, reusable extensions 

Low code applications are built as composable components that can be reused across processes and business units. This reduces the proliferation of one‑off extensions and supports scalable growth as new requirements emerge, without recreating technical debt outside the core.

4. Simplify upgrades through clear separation 

By operating independently of the S/4HANA lifecycle and integrating through released interfaces, low code extensions simplify upgrades and regression testing. At the same time, CIOs retain control by defining clear standards for how extensions are built, owned, and maintained.

In this operating model, SAP Business Technology Platform remains the secure and scalable foundation, while low code becomes the execution layer that turns clean ore from a theoretical principle into a practical and repeatable capability. By modularizing business logic outside the S/4HANA core, low code platforms can help CIOs actively manage and prevent technical debt instead of continuously accumulating it through tightly coupled customizations.

CLEVR’s Role: From clean core approach to working architecture

Understanding the clean core principle is one thing. Turning it into something that actually works across systems teams and business units is another.

With more than 30 years of experience delivering enterprise‑grade low‑code solutions using Mendix, CLEVR has helped multiple organizations translate clean‑core principles into working, business‑critical applications.

Instead of treating extensions as individual solutions built in isolation, at CLEVR we help define how extensions should be designed, connected to SAP, governed, and maintained over time.

This includes:

• defining clear architectural patterns aligned with SAP’s clean‑core principles
• advising on integration strategies using standard APIs and events
• establishing ownership and governance models across IT and business teams
• setting development standards that balance speed with control
• designing lifecycle processes so extensions remain manageable, upgrade‑safe, and predictable as they grow in scope and importance

Clean core is only valuable if you can execute it safely

A clean core is not a theoretical goal. For CIOs, it is a promise that business‑critical systems will remain stable, upgradeable, and secure while the organization continues to operate and evolve. But that promise only holds if the logic that keeps the business running can be rebuilt and extended with confidence.

 This is where experience matters. Building side‑by‑side applications for SAP landscapes requires more than tooling. It requires architectural judgment, governance discipline, and a delivery model that works under real business pressure. And with decades of experience delivering enterprise‑grade, business‑critical applications, CLEVR can deliver exactly that.

If you are planning or already navigating an S/4HANA migration and want to extend SAP safely without risking core business operations, our team is ready to help.

Contact CLEVR for a consultation to discuss how to build secure, business‑critical extensions that keep your core clean while your organization continues to move forward with confidence.

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